Lidar based 3D Tracking and State Estimation of Dynamic Objects
This addresses the need for accurate state estimation of other vehicles in autonomous driving systems, but it appears incremental as it shifts focus from ego-vehicle to non-ego vehicles in dynamic settings.
The paper tackles the problem of estimating the states of non-ego vehicles in dynamic scenes, which is crucial for motion planning and decision-making, by focusing on dynamic environments rather than static ones.
State estimation of oncoming vehicles: Earlier research has been based on determining states like position, velocity, orientation , angular velocity, etc of ego-vehicle. Our approach focuses on estimating the states of non-ego vehicles which is crucial for Motion planning and decision-making. Dynamic Scene Based Localization: Our project will work on dynamic scenes like moving ego (self) and non-ego vehicles. Previous methods were focused on static environments.